Improving Web Service Recommendation using Clustering and Model-Based Methods

Abstract

Abstract: With the development of the world wide web (WWW), the number of people who can deal with their work through the Internet, is increasing and it helps to do their tasks effectively and efficiently. In this case, a very important task is fulfilled by Web services. But the main problem is users struggling to select their favourite Web services quickly and accurately among available Web services. Web service recommendations help to solve this problem successfully. In this paper, we used collaborative filtering (CF)-based recommendation technique, but it suffers from the data sparsity and cold-start problem. Therefore, we applied an ontologybased clustering approach to overcome these problems. It effectively increased the data density by assuming the missing user preferences comparing the history of user favoured domains. Then, user ratings are predicted based on the model-based approach such as singular value decomposition (SVD). The result showed that the clustering approach can overcome the CF problems effectively and the SVD method can predict user ratings with lower prediction error compared with existing approaches.